Abstract
Mammographic scoring of density and texture are established methods to relate to the risk of breast cancer. We present a method that learns descriptive features from unlabeled mammograms and, using these learned features as the input to a simple classifier, address the following tasks: i) breast tissue segmentation ii) scoring of percentage mammographic density (PMD), and iii) scoring of mammographic texture (MT). Our results suggest that the learned PMD scores correlate well to manual ones, and that the learned MT scores are more related to future cancer risk than both manual and automatic PMD scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S.Y., et al.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. The Lancet 380(9859), 2095–2128 (2013)
McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidemiology Biomarkers & Prevention 15(6), 1159–1169 (2006)
Boyd, N.F., Martin, L.J., Bronskill, M., Yaffe, M.J., Duric, N., Minkin, S.: Breast tissue composition and susceptibility to breast cancer. Journal of the National Cancer Institute 102(16), 1224–1237 (2010)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)
Tabár, L., Duffy, S.W., Vitak, B., Chen, H.-H., Prevost, T.C.: The natural history of breast carcinoma. Cancer 86(3), 449–462 (1999)
Petersen, K., Nielsen, M., Ng, A.Y., Diao, P., Vachon, C.M., Karssemeijer, N., Lillholm, M.: Unsupervised deep learning for image segmentation and mammographic risk scoring. IEEE Transactions on Medical Imaging (in review)
Otten, J.D., Karssemeijer, N., Hendriks, J.H., Groenewoud, J.H., Fracheboud, J., Verbeek, A.L., de Koning, H.J., Holland, R.: Effect of recall rate on earlier screen detection of breast cancers based on the dutch performance indicators. Journal of the National Cancer Institute 97(10), 748–754 (2005)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Ranzato, M., Poultney, C.S., Chopra, S., LeCun, Y.: Efficient learning of sparse representations with an energy-based model. In: NIPS, pp. 1137–1144 (2006)
Nielsen, M., Karemore, G., Loog, M., Raundahl, J., Karssemeijer, N., Otten, J., Karsdal, M., Vachon, C., Christiansen, C.: A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. Cancer Epidemiology 35(4), 381–387 (2011)
Highnam, R., Brady, S.M., Yaffe, M.J., Karssemeijer, N., Harvey, J.: Robust breast composition measurement - volparaTM. In: MartÃ, J., Oliver, A., Freixenet, J., MartÃ, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 342–349. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Petersen, K., Nielsen, M., Diao, P., Karssemeijer, N., Lillholm, M. (2014). Breast Tissue Segmentation and Mammographic Risk Scoring Using Deep Learning. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)